ETL is short for “Extract, Transform and Load” and is essential to the effectiveness of business intelligence (BI) processes and systems. The ETL process allows for the collection and processing of data from different sources in one place. This data is then analysed to provide insight into business performance, highlight opportunities for improvement and facilitate informed decision making that will impact business revenue and profitability.
What does the ETL process include?
The ETL process extracts data from different source systems and the data is then transformed and enriched by applying calculations, concatenations, etc. This transformed data is then loaded into the data warehouse system. The data warehouse system is often specifically structured to an organisation’s analytical needs and stores all necessary and essential data collected from the ETL process. The ETL process can be defined using three steps, these include:
Step 1: Extraction
- Data is extracted from a number of different sources and put into a staging area. Telcos collect extensive amounts of data from different sources including call detail records, mobile phone usage, network equipment, server logs, billing and social network data. This provides a lot of information relating to customers and the network which is why it is important to extract this data.
Step 2: Transformation
- The data extracted from source servers is often raw and unusable in its original form. This data needs to be cleaned, mapped and transformed.
- The staging area is used to cleanse and organise data into a usable and insightful format which is then converted into a single system format.
- There are several different validations performed during this stage. Some of which include:
- Filtering – Selecting only specific columns to load
- Using rules and lookup tables for data standardisation
- Character set conversion and encoding handling
- Conversion of Units of Measurements that include date-time conversion, currency conversions, numerical conversions, etc.
Step 3: Loading
- Once the data has been cleaned and transformed, it can be moved from the staging area and into the data warehouse.
- This can either be done in batches or all at once, depending on your business needs and the ETL tools chosen.
Step 4: Analyse
- The loaded data is then used to run analytics and provide insight into different areas of the business. In the case of Telcos, it would be used to provide information relating to network revenue contributors, churn management, KPIs, spend etc.
The main aim of ETL is to ensure that the data extracted from sources can be optimised for analytics and insights which will be used to enhance business productivity and profitability.
Traditional vs Modern ETL processes
ETL processes have been used for a number of years by companies who have realised that integrating data from different systems and sources would provide them with business intelligence and a strategic edge. But this would only be the case if the data was clean, structured, high quality and integrated into a single location also known as a data warehouse. The result was the development of software to facilitate the ETL process.
The traditional ETL process required specialised IT staff to develop and use on-premises databases and data pipelines. This process proved to be incredibly time-consuming, and the technology was difficult to scale as data volumes grew. This solution is often inflexible and unable to adapt to new customisation needs or analytics requirements. Traditional ETL is also very costly due to the need for large data sets, faster processes, and resources specifically for ETL processes. The traditional ETL process was often viewed as being too slow and costly for businesses to maintain and didn’t allow for real-time analysis.
With traditional ETL being inflexible, expensive and too slow for an agile data-driven organisation, a new solution was needed. Many organisations have turned to new technology and, what is now referred to as, the modern ETL process. Modern ETL processes can either be on-premises or cloud-based solutions, which have proven to be much faster and more scalable. This Modern ETL process can import and export both structured and unstructured data from almost any source, enhancing flexibility. This solution leverages the benefits of the cloud to provide accessibility with improved security and delivers easy scalability at an affordable cost for business.
Choosing the right ETL tools
There is no doubt that ETL processes are essential in obtaining accurate business analytics to gain better insight into business performance and opportunities in order to enhance revenue. For this process to be effective, you need to choose the right modern ETL tool for your business. This choice is mainly dependent on the size of your organisation, cost, location of storage, and the amount of data that needs to be processed. These tools include:
- Enterprise – this solution is used by large companies and is at a higher cost compared to other available options. These solutions often offer a wide range of choice of Data Warehouse solutions for both on-premises and in the cloud. Examples of this type of tool include Oracle Data Integrator, SAP Data Services, IBM Infosphere DataStage, SAS Data Manager, Microsoft SQL Server Integration Services – SSIS.
- Custom ETL Programming – this ELT tool is perfect for companies wanting to develop their own tools for enhanced flexibility. Examples of languages used for this programming: Java, .Net, Python, etc.
- Open Source – these are free open source tools for all users. Examples: Pentaho Data Integration, Talend Open Studio.
- Cloud Service – Tools from Google, Microsoft or Amazon that have their own ETL services in the cloud. Examples: Amazon AWS Glue, Microsoft Azure Data Factory, Google Cloud Dataflow, Amazon AWS EMR.
What components to look out for when choosing an ETL tool?
From the above, it is clear that there are a number of tools available, but there are critical components that need to be considered before choosing one of them. These include:
- Support for change data capture (CDC) (a.k.a. binlog replication): incremental loading allows you to update your analytics warehouse with new data without having to do a full reload of the entire data set.
- Auditing and logging: you need detailed logging within the ETL pipeline to ensure that data can be audited after it is loaded and that errors can be debugged.
- Handling of multiple source formats: to pull in data from diverse sources such as Salesforce’s API, back-end financials applications, and databases such as MySQL and MongoDB. Your process needs to be able to handle a variety of data formats.
- Fault tolerance: the ETL systems need to be able to recover, making sure that data can make it from one end of the pipeline to the other even when the first run encounters problems.
- Notification support: a notification system will alert you when data is not accurate.
- Low latency: for real-time decision-making data needs to be as updated as possible. While there will be latency constraints imposed by particular source data integrations, data should flow through your ETL process with as little latency as possible.
- Scalability: all components of an ETL process should scale to support the growing needs of your business
- Accuracy: every data point should be auditable at every stage in your process.
Benefits of ETL to your business
ETL is a complex process, but once you have chosen the right tool and capabilities, you will have access to advanced analytics that allow for:
- Data that you can use – how the ETL process structures data ensures that data is represented visually in a way that is straightforward and simple to interpret. The data is also available in almost real-time, which facilitates business agility and flexibility.
- Heightened Business Intelligence – this technology provides you with advanced analytics and access to essential business data. This data will allow you to make data-driven decisions regarding, service offerings, customer churn, spend, monthly targets and more.
- Enhanced profitability – with a birds-eye view of all data relating to your business, you will be able to identify opportunities for growth as well as issues or concerns that may be hampering operations and affecting profitability.
- Improved reporting capabilities – the ETL process provides you with the ability to pull faster and more accurate reports in almost real-time.
Conclusion
Analytics are your businesses biggest digital asset, but there are no analytics without the ETL process. The ETL process ultimately extracts and cleans your data from several sources and puts it into a usable format which is then used to pull important business-related analytics in a simple and easy to interpret way. Using this data, you are given insight into business performance, what services and offerings are working and what is not, where your customer churn is coming from, where there is an opportunity for growth and more. Data, business intelligence and analytics provide your business with the tools needed to drive growth.
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Experienced in the Telecoms & IT industries, encompassing services and solutions from the traditional to the evolved digital communication networks, embracing profound knowledge and proficiency in both the technical and sales disciplines.  Product Management, including construct, software lifecycle, roadmap strategy and market leadership of Adapt IT’s portfolio of Next Gen Value Added Services (NG-VAS) solution stream.
